Aims

This module provides an introduction to NLP research centered
around lexical semantics (i.e., aspects of the meaning of words and
relations between word meanings). Relevant phenomena are described
theoretically, followed by a description of algorithms for
determination of meaning and detection of text structure are
presented. Special attention is given to adequate evaluation methods
in each area. Applications are also discussed, where appropriate.

Note: students are expected to take L90 Overview to Natural Language Processing in parallel with this module.

Syllabus

Session 1: Background to lexical semantics and word senses.
What is word meaning, and what are word senses? What does a
lexicographer do? Psycholinguistic background, linguistic tests for
ambiguity

Session 2: Word sense disambiguation.
Supervised and unsupervised methods of determining the sense of a
word. How can a computer learn when "bass" is a fish, and when it is a
musical instrument? How does a piece of text "hang together"
lexically? How can we segment a piece of text according to the topics
it discusses?

Session 4: Distributional semantics and semantic spaces.
How words can be represented "by the company they keep". The vector
space model, and dimensionality reduction models. LSI, Topic models.

Session 5: Verb classes and clustering.
Frame Semantics, Semantic Role Labelling, selectional preferences. In
which respect can verb meanings be similar to each other (e.g.,
purchase, buy, sell, lend)? How can we represent these similarities?

Session 6: Figurative language.
What are metaphors, metonymies and similes? How can a machine
recognise and interpret figurative language?

Session 7: Antonymy and sentiment detection.
What does it mean for a piece of text to display negative or positive
sentiment, and how could it be automatically recognised? Which types
of words have an "opposite" -- and what does that mean in each case?